Application of Multi-Index Stochastic Collocation to Part-scale Thermomechanical Analysis of Laser-based Powder Bed Fusion of Metals

  • Chiappetta, Mihaela (University of Pavia)
  • Piazzola, Chiara (CNR-IMATI)
  • Carraturo, Massimo (University of Pavia)
  • Tamellini, Lorenzo (CNR-IMATI)
  • Reali, Alessandro (University of Pavia)
  • Auricchio, Ferdinando (University of Pavia)

Please login to view abstract download link

Laser-based Powder Bed Fusion of Metals (PBF-LB/M) is an additive manufacturing technology suitable for producing metal components with complex geometries and remarkable mechanical performances. However, due to the inherent variability of process parameters, several sources of uncertainty hinder a full understanding of the complex process-structure-property relationships. An effective approach to address the above issue is to adopt rigorous Uncertainty Quantification (UQ) techniques to study the propagation of uncertainties in PBF-LB/M processes. The present work employs the Multi-Index Stochastic Collocation (MISC) approach to construct multi-fidelity surrogate models for part-scale thermomechanical analysis of a PBF-LB/M process of an Inconel 625 beam [1]. We take advantage of the computational efficiency of the MISC method to construct a multi-fidelity surrogate model [2]. In particular, using an inverse Bayesian approach, we build a MISC model to quantify and calibrate uncertainties in the activation temperature and in the powder convection coefficient. Afterwards, we employ the calibrated parameters to build a second MISC model to accurately predict the residual strains of the beam, using a posterior-based forward UQ procedure. This work extends [3], where a single fidelity surrogate model based on sparse grids was used. The proposed workflow is now experimentally validated with the public data provided by the National Institute of Standards and Technology for the AMBench2018-01 experiment. REFERENCES [1] Carraturo, M., Jomo, J., Kollmannsberger, S., Reali, A., Auricchio, F., Rank, E., Modeling and experimental validation of an immersed thermo-mechanical part-scale analysis for laser powder bed fusion processes. Additive Manufacturing, 36, 101498 (2020). [2] Piazzola, C., Tamellini, L., Pellegrini, R., et al. Comparing multi-index stochastic collocation and multi-fidelity stochastic radial basis functions for forward uncertainty quantification of ship resistance. Engineering with Computers (2022). [3] Chiappetta, M., Piazzola, C., Carraturo, M., et al. Inverse and forward sparse-grids-based uncertainty quantification analysis of laser-based powder bed fusion of metals. Submitted to the International Journal of Mechanical Sciences.